Unraveling the insomnia web: a network analysis of insomnia and psychological symptoms in good and poor sleepers among young adults

Introduction: Poor sleep quality and insomnia are major public health concerns, often associated with mental health problems and particularly prevalent among young adults. While the relationship between insomnia and psychological distress is well-documented, the symptom-level interactions underlying these associations remain largely unexplored. Psychometric network analysis, a method for assessing large-scale interactions among sets of variables and identifying influential nodes within symptom networks, was employed in this study to investigate the relationships between insomnia symptoms, depression, anxiety, and stress in a cohort of Italian university students, focusing on differences between good and poor sleepers. Methods: Participants (n = 1,234, mean age: 23.3 ± 2.4 years) were classified as good sleepers (GS; n = 406) or poor sleepers (PS; n = 808) based on the Pittsburgh Sleep Quality Index. Gaussian Graphical Models were used to estimate network structures for each group, with insomnia symptoms (Insomnia Severity Index items) and subscale scores from the 21-item Depression Anxiety Stress Scale and the 10-item Perceived Stress Scale as nodes. Results: The PS network showed greater density (26/55 vs. 19/55) and more connections linking insomnia and distress symptoms. Expected influence, a measure of node centrality, identified worry about sleep, difficulty maintaining sleep, and stress symptoms as the most central nodes in both groups, while anxiety symptoms were more influential in the PS network, underscoring their role in the interplay between poor sleep and distress. Conclusion: These findings highlight the transdiagnostic nature of insomnia and support the utility of network analysis in identifying key symptoms that may serve as targets for interventions addressing both sleep and psychological problems.